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With an increase in the number of users and the variety of applications with diverse quality of service (QoS) requirements, a more sophisticated radio resource scheduling (RRS) method is needed in 5G networks compared to previous generations. To address this challenge, a growing body of research formulates the RRS problem as a Markov decision process (MDP) and aims to solve it using deep reinforcement learning (DRL). In this dissertation, we first provide a review of existing DRL-based 5G radio resource schedulers, with emphasis on schedulers that aim to minimize the average user delay and follow a fine-grained approach, i.e., use DRL to directly allocate resource blocks (RBs) to user equipments (UEs). Then, we discuss ways to improve upon this early-stage research and identify some open problems that must be solved to make DRL a viable solution to the downlink scheduling problem in 5G networks. The success of any DRL-based approach depends on several factors, including how well the reward signal frames the end goal and how well it assesses progress in reaching that goal. In practice, designing a reward signal is often an informal trial-and-error search for a signal that produces the desired results. Despite the fact that the reward function is a critical component of an MDP, currently there is no systematic study comparing how different reward functions affect network performance. To this end, we first carry out a comparative study of the delay and overflow performance of several reward functions that aim to minimize sum-delay experienced by users in a single-carrier multi-user downlink scheduling scenario where each user has a finite buffer. Through extensive simulations under different traffic and channel conditions, we identify a reward function that can achieve near optimal delay with up to 55 − 67% fewer packet drops than the other investigated options, and does not require any tuning. We then carry out a similar study in a more common multi-carrier multi-user scheduling scenario where each user has an infinite buffer (i.e., there are no packet overflows). With the help of simulations, we draw important conclusions regarding reward design and show that the reward designs under consideration outperform the benchmark rule-based scheduler. Depending on how RBs are scheduled, there are two approaches to RRS. In the first approach, RBs are scheduled jointly. In the second approach, RBs are scheduled sequentially. The sequential approach reduces the computational complexity of the problem by reducing the action space. Thus, we convert the joint scheduling problem into a sequential scheduling problem and show how the optimal value function for the joint scheduling problem is decomposed after converting it to a sequential scheduling problem. With the help of extensive simulations under different traffic and channel conditions, we show that the sequential RRS schedulers not only reduce the computational complexity of the problem, but also perform better than the joint RRS schedulers. 

We consider the uplink of a large-scale active terrestrial cellular network operating in the restricted L-band (1400–1427 MHz) with NASA's Soil Moisture Active Passive (SMAP) satellite. In our previous work, we used stochastic geometry to derive the statistical properties of radio frequency interference (RFI) at SMAP that originates from the terrestrial network. We demonstrated that, while it is crucial to prevent RFI from interfering with the main-lobe of SMAP's antenna, its low-gain side-lobe can tolerate the coexistence of a significant number of active devices on the same channel with low to moderate RFI impact on SMAP's measurements, ensuring minimal disruption to its sensing accuracy. In this paper, we extend our previous work by analyzing the Sensing Outage Probability (SOP), which is a novel metric that represents the probability of RFI exceeding the acceptable error threshold for a given passive remote sensing mission. We specifically assess the statistical characteristics, namely, the expected value and variance, of RFI induced on the SMAP satellite by clusters of 5G cellular User Equipment (UE), considering variations in the number of UEs and their transmission powers. Subsequently, we derive the SOP for each configuration and demonstrate that a significant number of UEs can operate co-channel with the passive remote sensor while ensuring that the induced RFI remains below a given threshold (in Kelvin) with high certainty. 

As next-generation communication services and satellite systems expand across diverse frequency bands, the escalating utilization poses heightened interference risks to passive sensors crucial for environmental and atmospheric sensing. Consequently, there is a pressing need for efficient methodologies to detect, characterize, and mitigate the harmful impact of unwanted anthropogenic signals known as radio frequency interference (RFI) at microwave radiometers. One effective strategy to reduce such interference is to facilitate the coexistence of active and passive sensing systems. Such approach would greatly benefit from a testbed along with a dataset encompassing a diverse array of scenarios under controlled environment. This study presents a physical environmentally controlled testbed including a passive fully calibrated L-band radiometer with a digital back-end capable of collecting raw in-phase/quadrature (IQ) samples and an active fifth-generation (5G) wireless communication system with the capability of transmitting waveforms with advanced modulations. Various RFI scenarios such as in-band, transition-band, and out-of-band transmission effects are quantified in terms of calibrated brightness temperature. Raw radiometer and 5G communication samples along with preprocessed time-frequency representations and true brightness temperature data are organized and made publicly available. A detailed procedure and publicly accessible dataset are provided to help test the impact of wireless communication on passive sensing, enabling the scientific community to facilitate coexistence research and quantify interference effects on radiometers. 

In our ever-expanding world of advanced satellite and communications systems, there's a growing challenge for passive radiometer sensors used in the Earth observation like 5G. These passive sensors are challenged by risks from radio frequency interference (RFI) caused by anthropogenic signals. To address this, we urgently need effective methods to quantify the impacts of 5G on Earth observing radiometers. Unfortunately, the lack of substantial datasets in the radio frequency (RF) domain, especially for active/passive coexistence, hinders progress. Our study introduces a controlled testbed featuring a calibrated L-band radiometer and a 5G wireless communication system. In a controlled chamber, this unique setup allows us to observe and quantify transmission effects across different frequency bands. By creating a comprehensive dataset, we aim to standardize and benchmark both wireless communication and passive sensing. With the ability to analyze raw measurements, our testbed facilitates RFI detection and mitigation, fostering the coexistence of wireless communication and passive sensing technologies while establishing crucial standards. 

There is insufficient wireless frequency spectrum to support the continued growth of active wireless technologies and devices. This has provoked extensive research on spectrum coexistence. One case that has gained limited attention in this course is using currently banned frequency bands for active wireless communications. One such option is the 27 MHz-wide narrowband portion of the L-band from 1.400 to 1.427 GHz, which is exclusively devoted to space-borne passive radiometry for remote sensing and radio astronomy. Radio regulations currently prohibit active wireless communications and radars from operating in this band to avoid radio frequency interference (RFI) on highly noise-sensitive passive radiometry equipment. The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) satellite is one of the latest space-borne remote sensing missions that evaluates global soil moisture by passive scanning of the thermal emissions of the earth in this frequency band. In this paper, we investigate the opportunistic temporal use of this 27 MHz-wide passive radiometry band for active wireless transmissions when there is no Line of Sight (LoS) between SMAP and a terrestrial wireless network. We use MATLAB simulations to determine the fraction of time that SMAP has LoS (and non-LoS) with a terrestrial wireless cell at different Earth latitudes based on SMAP’s orbital characteristics. We also investigate the severity of RFI induced on SMAP in the presence of a terrestrial cluster of 5G cells with LoS. 

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.